3D structural models of transmembrane proteins.
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ABSTRACT:
SUBMITTER: de Brevern AG
PROVIDER: S-EPMC3178832 | biostudies-literature | 2010
REPOSITORIES: biostudies-literature
Publications
3D structural models of transmembrane proteins.
Methods in molecular biology (Clifton, N.J.) 20100101
Transmembrane proteins are macromolecules implicated in major biological processes and diseases. Because of their specific neighborhood, few transmembrane protein structures are currently available. The building of structural models of transmembrane proteins is a major research area. Because of the lack of available 3D structures, automatic homology modeling is not an efficient way of proposing pertinent structural models. Hence, most of the structural models of transmembrane proteins are develo ...[more]
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Project description:BackgroundThis study is motivated by the following three considerations: a) the physico-chemical properties of transmembrane (TM) proteins are distinctly different from those of globular proteins, necessitating the development of specialized structure prediction techniques, b) for many structural features no specialized predictors for TM proteins are available at all, and c) deep learning algorithms allow to automate the feature engineering process and thus facilitate the development of multi-target methods for predicting several protein properties at once.ResultsWe present AllesTM, an integrated tool to predict almost all structural features of transmembrane proteins that can be extracted from atomic coordinate data. It blends several machine learning algorithms: random forests and gradient boosting machines, convolutional neural networks in their original form as well as those enhanced by dilated convolutions and residual connections, and, finally, long short-term memory architectures. AllesTM outperforms other available methods in predicting residue depth in the membrane, flexibility, topology, relative solvent accessibility in its bound state, while in torsion angles, secondary structure and monomer relative solvent accessibility prediction it lags only slightly behind the currently leading technique SPOT-1D. High accuracy on a multitude of prediction targets and easy installation make AllesTM a one-stop shop for many typical problems in the structural bioinformatics of transmembrane proteins.ConclusionsIn addition to presenting a highly accurate prediction method and eliminating the need to install and maintain many different software tools, we also provide a comprehensive overview of the impact of different machine learning algorithms and parameter choices on the prediction performance. AllesTM is freely available at https://github.com/phngs/allestm.
| S-EPMC7291640 | biostudies-literature
Project description:The stoichiometry of the first shell of lipids interacting with a transmembrane protein is defined operationally by the population of spin-labeled lipid chains whose motion is restricted directly by the protein. Interaction stoichiometries have been determined experimentally for a wide range of alpha-helical integral membrane proteins by using spin-label ESR spectroscopy. Here, we determine the spatially defined number of first-shell lipids at the hydrophobic perimeter of integral membrane proteins whose 3D structure has been determined by X-ray crystallography and lipid-protein interactions characterized by spin-labeling. Molecular modeling is used to build a single shell of lipids surrounding transmembrane structures derived from the PDB. Constrained energy optimization of the protein-lipid assemblies is performed by molecular mechanics. For relatively small proteins (up to 7-12 transmembrane helices), the geometrical first shell corresponds to that defined experimentally by perturbation of the lipid-chain dynamics. For larger, multi-subunit alpha-helical proteins, the lipids perturbed directly by the protein may either exceed or be less in number than those that can be accommodated at the intramembranous perimeter. In these latter cases, the motionally restricted spin-labeled lipids can be augmented by intercalation, or can correspond to a specific subpopulation at the protein interface, respectively. For monomeric beta-barrel proteins, the geometrical lipid stoichiometry corresponds to that determined from lipid mobility for a 22-stranded barrel, but fewer lipids are motionally restricted than can be accommodated around an eight-stranded barrel. Deviations from the geometrical first shell, in the beta-barrel case, are for the smaller protein with a highly curved barrel.
| S-EPMC2242517 | biostudies-literature
Project description:Transmembrane β-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and α-helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting β-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent β-strands at an accuracy of ∼70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand-strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of β-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases.
| S-EPMC4418893 | biostudies-literature
Project description:Proteins of the tetraspanin family contain four transmembrane domains (TM1-4) linked by two extracellular loops and a short intracellular loop, and have short intracellular N- and C-termini. While structure and function analysis of the larger extracellular loop has been performed, the organization and role of transmembrane domains have not been systematically assessed.Among 28 human tetraspanin proteins, the TM1-3 sequences display a distinct heptad repeat motif (abcdefg)n. In TM1, position a is occupied by structurally conserved bulky residues and position d contains highly conserved Asn and Gly residues. In TM2, position a is occupied by conserved small residues (Gly/Ala/Thr), and position d has a conserved Gly and two bulky aliphatic residues. In TM3, three a positions of the heptad repeat are filled by two leucines and a glutamate/glutamine residue, and two d positions are occupied by either Phe/Tyr or Val/Ile/Leu residues. No heptad motif is apparent in TM4 sequences. Mutations of conserved glycines in human CD9 (Gly25 and Gly32 in TM1; Gly67 and Gly74 in TM2) caused aggregation of mutant proteins inside the cell. Modeling of the TM1-TM2 interface in CD9, using a novel algorithm, predicts tight packing of conserved bulky residues against conserved Gly residues along the two helices. The homodimeric interface of CD9 was mapped, by disulfide cross-linking of single-cysteine mutants, to the vicinity of residues Leu14 and Phe17 in TM1 (positions g and c) and Gly77, Gly80 and Ala81 in TM2 (positions d, g and a, respectively). Mutations of a and d residues in both TM1 and TM2 (Gly25, Gly32, Gly67 and Gly74), involved in intramolecular TM1-TM2 interaction, also strongly diminished intermolecular interaction, as assessed by cross-linking of Cys80.Our results suggest that tetraspanin intra- and intermolecular interactions are mediated by conserved residues in adjacent, but distinct regions of TM1 and TM2. A key structural element that defines TM1-TM2 interaction in tetraspanins is the specific packing of bulky residues against small residues.
| S-EPMC1190194 | biostudies-literature
Project description:MotivationTransmembrane β-barrels exist in the outer membrane of gram-negative bacteria as well as in chloroplast and mitochondria. They are often involved in transport processes and are promising antimicrobial drug targets. Structures of only a few β-barrel protein families are known. Therefore, a method that could automatically generate such models would be valuable. The symmetrical arrangement of the barrels suggests that an approach based on idealized geometries may be successful.ResultsHere, we present tobmodel; a method for generating 3D models of β-barrel transmembrane proteins. First, alternative topologies are obtained from the BOCTOPUS topology predictor. Thereafter, several 3D models are constructed by using different angles of the β-sheets. Finally, the best model is selected based on agreement with a novel predictor, ZPRED3, which predicts the distance from the center of the membrane for each residue, i.e. the Z-coordinate. The Z-coordinate prediction has an average error of 1.61 Å. Tobmodel predicts the correct topology for 75% of the proteins in the dataset which is a slight improvement over BOCTOPUS alone. More importantly, however, tobmodel provides a Cα template with an average RMSD of 7.24 Å from the native structure.AvailabilityTobmodel is freely available as a web server at: http://tobmodel.cbr.su.se/. The datasets used for training and evaluations are also available from this site.
| S-EPMC3371865 | biostudies-literature
Project description:BACKGROUND: A large proportion of an organism's genome encodes for membrane proteins. Membrane proteins are important for many cellular processes, and several diseases can be linked to mutations in them. With the tremendous growth of sequence data, there is an increasing need to reliably identify membrane proteins from sequence, to functionally annotate them, and to correctly predict their topology. RESULTS: We introduce a technique called structural fragment clustering, which learns sequential motifs from 3D structural fragments. From over 500,000 fragments, we obtain 213 statistically significant, non-redundant, and novel motifs that are highly specific to alpha-helical transmembrane proteins. From these 213 motifs, 58 of them were assigned to function and checked in the scientific literature for a biological assessment. Seventy percent of the motifs are found in co-factor, ligand, and ion binding sites, 30% at protein interaction interfaces, and 12% bind specific lipids such as glycerol or cardiolipins. The vast majority of motifs (94%) appear across evolutionarily unrelated families, highlighting the modularity of functional design in membrane proteins. We describe three novel motifs in detail: (1) a dimer interface motif found in voltage-gated chloride channels, (2) a proton transfer motif found in heme-copper oxidases, and (3) a convergently evolved interface helix motif found in an aspartate symporter, a serine protease, and cytochrome b. CONCLUSIONS: Our findings suggest that functional modules exist in membrane proteins, and that they occur in completely different evolutionary contexts and cover different binding sites. Structural fragment clustering allows us to link sequence motifs to function through clusters of structural fragments. The sequence motifs can be applied to identify and characterize membrane proteins in novel genomes.
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Project description:BackgroundAllergenic proteins can cause IgE-mediated adverse reactions in sensitized individuals. Although the sequences of many allergenic proteins have been identified, bioinformatics data analysis with advanced computational methods and modeling is needed to identify the basis for IgE binding and cross-reactivity.ObjectiveWe aim to present the features and use of the updated Structural Database of Allergenic Proteins 2.0 (SDAP 2.0) webserver, a unique, publicly available resource to compare allergens using specially designed computational tools and new high-quality 3-D models for most known allergens.MethodsPreviously developed and novel software tools for identifying cross-reactive allergens using sequence and structure similarity are implemented in SDAP 2.0. A comprehensive set of high-quality 3-D models of most allergens was generated with the state-of-the-art AlphaFold 2 software. A graphics tool enables the interactive visualization of IgE epitopes on experimentally determined and modeled 3-D structures.ResultsA user can search for allergens similar to a given input sequence with the FASTA algorithm or the window-based World Health Organization/International Union of Immunological Societies (WHO/IUIS) guidelines on safety concerns of novel food products. Peptides similar to known IgE epitopes can be identified with the property distance tool and conformational epitopes by the Cross-React method. The updated database contains 1657 manually curated sequences including all allergens from the IUIS database, 334 experimentally determined X-ray or NMR structures, and 1565 3-D models. Each allergen/isoallergen is classified according to its protein family.ConclusionsSDAP provides access to the steadily increasing information on allergenic structures and epitopes with integrated bioinformatics tools to identify and analyze their similarities. In addition to serving the research and regulatory community, it provides clinicians with tools to identify potential coallergies in a sensitive patient and can help companies to design hypoallergenic foods and immunotherapies.
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Project description:Phosphorylation is a common post-translational modification that plays important roles in a wide range of biochemical and cellular processes. Many enzymes and receptors can be switched "on" or "off" by conformational changes induced by phosphorylation. The phosphorylation process is mediated by a family of enzymes called kinase. Currently, more than 1,000 different kinases have been identified in Arabidopsis thaliana proteome. Kinases interact with each other and with many regulatory proteins forming phosphorylation networks. These phosphorylation networks modulate the signaling processes and control the functions of cells. Normally, kinases phosphorylate serines, threonines, and tyrosines. However, in many proteins, not all of these 3 types of amino acids can be phosphorylated. Therefore, identifying the phosphorylation sites and the possible phosphorylation events is very important in decoding the processes of regulation and the function of phosphorylation networks. In this study, we applied computational and bioinformatics tools to characterize the association between phosphorylation events and structural properties of corresponding proteins by analyzing more than 50 trans-membrane proteins from Arabidopsis thaliana. In addition to the previously established conclusion that phosphorylation sites are closely associated with intrinsic disorder, we found that the phosphorylation process may also be affected by solvent accessibility of phosphorylation sites and further promoted by neighboring modification events.
| S-EPMC5424800 | biostudies-literature
Project description:α-helical transmembrane (TM) proteins play an important role in many critical and diverse biological processes, and specific associations between TM helices are important determinants for membrane protein folding, dynamics and function. In order to gain insights into the above phenomena, it is necessary to investigate different types of helix-packing modes and interactions. However, such information is difficult to obtain because of the experimental impediment and a lack of a well-annotated source of helix-packing folds in TM proteins. We have developed the TMPad (TransMembrane Protein Helix-Packing Database) which addresses the above issues by integrating experimentally observed helix-helix interactions and related structural information of membrane proteins. Specifically, the TMPad offers pre-calculated geometric descriptors at the helix-packing interface including residue backbone/side-chain contacts, interhelical distances and crossing angles, helical translational shifts and rotational angles. The TMPad also includes the corresponding sequence, topology, lipid accessibility, ligand-binding information and supports structural classification, schematic diagrams and visualization of the above structural features of TM helix-packing. Through detailed annotations and visualizations of helix-packing, this online resource can serve as an information gateway for deciphering the relationship between helix-helix interactions and higher levels of organization in TM protein structure and function. The website of the TMPad is freely accessible to the public at http://bio-cluster.iis.sinica.edu.tw/TMPad.
| S-EPMC3013749 | biostudies-literature
Project description:MotivationMembrane proteins are an important class of biological macromolecules involved in many cellular key processes including signalling and transport. They account for one third of genes in the human genome and >50% of current drug targets. Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap. However, as many empirical methods have been trained on experimental structural data, which is biased towards soluble globular proteins, their accuracy for transmembrane proteins is often limited.ResultsWe developed a local model quality estimation method for membrane proteins ('QMEANBrane') by combining statistical potentials trained on membrane protein structures with a per-residue weighting scheme. The increasing number of available experimental membrane protein structures allowed us to train membrane-specific statistical potentials that approach statistical saturation. We show that reliable local quality estimation of membrane protein models is possible, thereby extending local quality estimation to these biologically relevant molecules.Availability and implementationSource code and datasets are available on request.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4147910 | biostudies-literature